To evaluate the performance of 3D-MPE in complex scenarios, we perform experiments on MuPoTS-3D and compare it to current state-of-the-art methods, as shown in Table REF . After root alignment with the ground-truth poses, our model achieves higher performance and outperforms the SOTA[1]} on \(\text{PCK}_\text{rel}\)  by \(1.3\%\) . The results show that our person-centric 3D-MPE outperforms the SOTA on \(\text{PCK}_\text{rel}\)  by \(2.7\%\)  so that our structured 2.5D Pose can significantly improve the performance of the 2.5D pose. Additionally, our method is superior to the most top-down methods and comparable with the SOTA top-down methods on person-centric 3D-MPE metrics for matched ground truths. Since the existing top-down methods require an additional SOTA human detector, such as Mask R-CNN model [2]}, the metrics on \(\text{PCK}_\text{rel}\)  show an advantage for recognizing instances, but at the expense of computational complexity. We also perform camera-centric evaluation using \(\text{PCK}_\text{abs}\) , where we outperform the SOTA by 0.9%. To evaluate our root joint localization performance, we additionally calculate \(\text{PCK}_\text{root}\)  to evaluate the 3D localization of people. Notably, we achieve better \(\text{PCK}_\text{root}\)  than SOTA by a large margin, which shows our method can alleviate inherent depth ambiguity issues. Furthermore, our method can achieve 97.1% on PCOD. It clearly indicates our method can correctly identify the ordinal depth and we analyze this mainly with the help of geometric information. The evaluation on MuPoTS-3D shows that our method outperforms the state-of-the-art methods in both 3D pose and 2.5D pose performance.
